Effect of Driving Environment on Drivers Eye Movements: Re-Analyzing Previously Collected Eye-tracker Data

Transcription

1 Paper No.: Effect of Driving Environment on Drivers Eye Movements: Re-Analyzing Previously Collected Eye-tracker Data *Corresponding author by Mr. Myunghoon Ko Zachry Department of Civil Engineering Texas A&M University, TAMU 3136 College Station, TX Phone: (979) Ms. Laura L. Higgins* Human Factors Group Texas Transportation Institute The Texas A&M University System 3135 TAMU College Station, TX Phone: (979) Fax: (979) Dr. Susan T. Chrysler Human Factors Group Texas Transportation Institute The Texas A&M University System 3135 TAMU College Station, TX Phone: (979) Fax: (979) Dr. Dominique Lord Zachry Department of Civil Engineering Texas A&M University, TAMU 3136 College Station, TX Phone: (979) Fax: (979) November 13, 2009 Word Count: 6395

2 ABSTRACT Eye-tracking video data that had been collected for a previous driving study was reexamined to analyze other aspects of driver eye movement. Drivers in the original study navigated a pre-set route at night while their eye movements were recorded using a headmounted eye-tracking system. For the new analysis, eye movements from eleven of the previous study s drivers were observed as they drove on selected segments of public roads. Four driving environments were examined: 1) a segment of highway without street lighting, 2) a segment of highway with street lighting, 3) the approach to a stopsign controlled intersection without street lighting, and 4) the approach to signalized intersection with street lighting. Researchers evaluated the probabilities of drivers looking at specific areas of the road scene using binary logistic regression modeling. In the original study, drivers had been instructed to watch for specific signs on the righthand side of the roadway, which likely affected their eye movement patterns. Despite this probable bias, drivers exhibited different eye movement patterns in the four driving environments examined. The probabilities of drivers looking at locations off the eyetracker s screen (away from the roadway) decreased when approaching intersections. Drivers eye movements significantly changed on close approach to the intersections, particularly when approaching the signalized intersection with the more complex traffic environment.

3 Ko, Higgins, Chrysler, and Lord 3 INTRODUCTION During recent years, the Texas Transportation Institute (TTI) has conducted on-road driving studies using eye-tracking equipment for analyzing drivers visual search patterns. Participants in the studies drove a specially instrumented vehicle at night while their eye movements ware recorded via a head-mounted eye-tracking device. Simultaneously, vehicle control data such as steering, braking, and throttling are recorded by the instrumented vehicle. Typically in these kinds of studies, only a fraction of the collected data is analyzed to answer the specific questions being asked in the sponsoring study. These data sets, however, are enormously rich because they contain an hour or more data of normal driving. This project mined one of these existing data sets to answer further safety questions. TTI s eye tracker was used to collect data from 24 drivers navigating a pre-set route at night as part of a Texas Department of Transportation (TxDOT) Research Management Committee (RMC) project which was completed in July 2008 (1). For the current study, these eye movement data were re-analyzed to study drivers eye-scanning behavior along other portions of the route. The intent of the research was twofold: to learn more about drivers eye-scanning behavior at intersections at night and to develop a better understanding of the challenges and limitations of re-analyzing data collected for a prior study. This paper describes a portion of the data re-analysis. Four segments of roadway driving were compared: 1) a segment of highway without street lighting, 2) a segment of highway with street lighting, 3) the approach to a stop-sign-controlled intersection without street lighting, and 4) the approach to a signalized intersection with street lighting. The hypothesis tested was that drivers exhibit different eye scanning patterns under different driving environments. BACKGROUND Eye movement is the driver s primary information gathering method. Eye movement is defined as the change in the direction of gaze of the eyes and the duration or dwell time on objects in the scene (2). Research tracking and analyzing driver eye movements was first conducted by Rockwell, Mourant, and their colleagues beginning in (3) Eye scanning movement is usually analyzed according to 1) the scanning pattern (i.e., what drivers scan in sequence), 2) the number of glances (i.e., how many times drivers look at an object), and 3) the duration of glances (i.e., how long drivers look at the object). Serafin (4) analyzed drivers daytime eye movements and found that drivers looked straight ahead at the road 59 percent of the time, to the right side of the road 15 percent of the time, and to the left side of the road 25 percent of the time. Eye movement patterns have been shown to change with changes in the driving environment. For example, Zwahlen (5) examined the effect of the STOP AHEAD sign on drivers eye scanning behavior and performance when approaching intersections. Particularly during nighttime driving, the STOP AHEAD sign provided a visual clue to the drivers regarding the upcoming STOP sign. Drivers looked more frequently and longer at the STOP sign when the STOP AHEAD sign was present, and approached the intersection with significantly lower speed when the sign was present than when it was not. Hancock et al. (6) found that drivers had higher frequencies of head movements for left and right turns than for through movements at an intersection, and concluded that

4 Ko, Higgins, Chrysler, and Lord 4 the greater workload for turning maneuvers required increased head movements to collect the needed visual information. Knodler and Noyce (7) researched eye scanning patterns at permissive left-turn signalized intersections. When opposing traffic was present, drivers mainly focused on opposing traffic; however, when opposing traffic was not present, drivers tried to find visual cues by scanning from right to left. Illumination also affects eye scanning movements; the average duration of glances on curved sections of road was longer at night than during the day, while average glance duration on straight sections of road was shorter at night (8). Street lighting has been shown to improve traffic safety at night; street lighting at rural intersections reduces the frequency and the severity of both single and multiple vehicle nighttime crashes (9). The probability of nighttime crashes without lighting is two times higher than when lighting is present at the intersection (10). METHODOLOGY Data Collection Procedure This project analyzed existing video data collected by the Texas Transportation Institute during an earlier sign legibility study that was part of a research project sponsored by the Texas Department of Transportation (1). Data on driver eye movements were gathered while study participants drove a pre-set route in College Station, Texas at night. An instrumented Toyota Highlander with an automatic transmission was used as the experimental vehicle. This vehicle integrates several independent systems to measure and record driving data and driver behavior, while providing the driver with as naturalistic a driving experience as possible. Trip distance is measured both through an on-board GPS system and a distance measuring instrument connected to the odometer cable. The Arrington Viewpoint Eye-Tracking System was used to measure eye positions and eye movements. This head-mounted eye-tracking system is equipped with two eye cameras with infrared lights, which illuminate drivers eyes and capture the movement of the pupils, and a high-resolution, low-light scene camera to collect accurate eye-glance information after dark. Figure 1 is a frame from the eye tracking video, showing the elapsed time from the start of the route, distance traveled in feet, and vehicle speed, as well as the focusing location showing where the driver is looking. Data collection for each participant began with a calibration process for the eyetracking system. The first step in calibration was to ensure that the eye-tracker apparatus fit comfortably and securely on the participant s head while the participant was seated in the driver s seat of the test vehicle. The data collector focused the eye-cameras directly on the eyes pupils and positioned the forward camera to capture the view directly forward from the participant s eye level. The eye-tracking system was calibrated using a 16-point grid, which was visible to the data collector on the eye-tracker s video screen, superimposed over the view from the forward scene camera. Using a laser pointer aimed at the side of building approximately 200 ft from the front of the vehicle, the data collector asked the participant to follow a dot of light reflected against the building while keeping his or her head as still as possible. The eye-tracker software recorded each target fixation to create a personalized eye-mapping grid. The eye-mapping grid was then used to calibrate the

5 Ko, Higgins, Chrysler, and Lord 5 equipment. After a successful calibration, the participant began the driving portion of the study. Green dot: Location of eye focusing FIGURE 1 Screen example from eye-scanning video. Participants in the original TxDOT-sponsored study were volunteers recruited from the local population. All participants held valid driver s licenses with no nighttime restrictions, and completed visibility tests before beginning data collection. The purpose of the original study was to analyze driver eye movements as they approached a number of experimental roadway signs along the right side of the road. Participants were instructed to actively search for road signs with the legend TEST SIGN which could appear anywhere along the course, but were always ground-mounted on the right shoulder. While nearly an hour of video was collected on each participant s eye movements, only a few seconds of video leading up to each of the experimental signs was analyzed at the time. This project returned to the video footage to analyze eye movements as drivers approached roadway intersections along the route. In this new analysis, eye movement data were coded from only 11 of the 24 original drivers. This was due in part to problems with the calibration or camera angle on several of the videos. Although the eye-tracker had been successfully calibrated to each participant at the start of the original study, an hour or more of driving activity tended to shift the position of the eye-tracking device on a driver s head and therefore the relative

6 Ko, Higgins, Chrysler, and Lord 6 position of the scene camera. As one of the analyzed intersections fell close to the end of the route, the shifts in position for many drivers by that point made determining the drivers exact focus positions difficult. The eleven participants (two female and nine male) whose videos were reanalyzed ranged in age from 23 to 76 years, and the median and mean were 58 and 52 years old, respectively. This study selected two roadway segments and two intersections for analyzing drivers eye-scanning behaviors. The two roadway segments were selected because they were similar roadway types with similar features (speed limit, number of lanes, presence of guide signs and a bridge/overpass) and different in lighting (one with street lighting, one without). The two intersections were likewise similar in some of their characteristics (smaller road intersecting highway, same speed limit on the approach to the intersection) while differing in others (lighting, signalization vs. stop-controlled). The researchers hypothesized that a difference in driver eye movements could be observed over the four different environments, apart from the drivers focus on roadway signs to the right. Each segment and intersection approach analyzed was 1000 feet in length. Segment 1 is an unlighted rural divided highway (speed limit: 65mph) and includes three guide signs and a bridge. Segment 2 is a lighted suburban divided highway (speed limit: 65mph) and includes four guide signs and one bridge. Intersection 1 is an unlit stop-controlled intersection, the driver approached on a 2-lane rural road toward an undivided highway; the approach included two guide signs before the stop sign (speed limit: 50mph). Intersection 2 is a lighted, suburban signalized intersection which the driver approached on a divided four-lane suburban boulevard (speed limit: 50mph). The approach to the intersection includes four guide signs, and the intersection is controlled by three signal heads on each leg. For each of the segments and intersections described, driver eye movements were analyzed over a distance of 1000 feet of vehicle travel in the case of the two intersections, for the 1000 feet leading up to the selected intersection. There were no buildings or driveways adjacent to the intersections and segments. Also, there was no lead vehicle for most cases because driving was done late at night. Table 1 summarizes the characteristics of the selected segments and intersections. Table 1 Segment and Intersection Characteristics Intersection 1 (unlighted) Intersection 2 (lighted) Segment 1 (unlighted) Segment 2 (lighted) Intersection of rural road (50 mph) and rural highway Stop sign for rural road with Cross Traffic Does Not Stop Two guide signs Intersection of suburban boulevard (50 mph) and suburban highway Signalized intersection Four guide signs Four-lane divided rural highway Speed limit 65 mph Three guide signs and one bridge Four-lane divided suburban highway Speed limit 65 mph Four guide signs and one bridge

7 Ko, Higgins, Chrysler, and Lord 7 Data Reduction Procedure The eye-tracking system maps driver eye movements on the video scene filmed by the eye-tracker s forward view camera, which recorded data at a rate of 30Hz. The average of the driver s left and right eye positions relative to this forward view is shown as a moving green dot on the video. To analyze eye movements for this study, the research team initially attempted to define a number of specific targets or zones within the forward view shown by the scene camera: roadway signs, right and left roadway edge lines, close and far points on the center lane, and so forth. However, it was soon discovered that these small, distinct zones could not be coded consistently in the constantly shifting scene. Additionally, it was often impossible to determine how far away a driver was looking; a glance at the midpoint of the forward scene could be a glance at a far-away point of the road or a glance at something above the road. (In contrast, the original study focused on drivers glances at specific road signs, which was a more straightforward scoring scheme). Ultimately, four larger zones of interest were defined; three of those zones can be seen in Figure 1. The data coder then manually reviewed each video and scored which zone the driver was looking at for the roadway segments of interest frame by frame. Zone C, or center, includes the lane is which the driver is traveling, as well as locations on and above the road that are directly forward in the driver s view. Zone R ( right ) is the area to the right of the driver s own lane, including the right-side road shoulder or edge, any lanes to the driver s right, and other objects on or near the roadway on the right side. Zone L ( left ) similarly includes lanes, road edges, and other objects to the left of the driver s own lane. Zone O ( off-screen ) is a code indicating that the driver s gaze left the field of view of the forward scene camera; usually this occurs when the driver looks at something inside the car (e.g., instrument panel) or in another direction that is sufficiently outside his/her forward field of view that the eye-tracker cannot record the gaze. The eye movements recorded by the eye-tracker system include fixations and transition movements. During an eye fixation, the green dot shown on the forward scene is still for a period of time (often only a fraction of a second), or may flick back to the same location several times, indicating that the driver is focusing on a particular target or location. A transition step is the movement of the eyes between one fixation and the following fixation, and can be seen on the video as a streak or tail trailing the green dot. In the analysis, transition steps (usually amounting to no more than 0.1 seconds) were included in the subsequent fixation time. Table 2 shows a sample of the coded data for drivers eye movements when approaching Intersection 1. In Table 2, Distance refers to the location at which the driver initiated a glance to a specific zone, measured from the end point of the segments or the stop bar at the intersections. In the first row, when the vehicle passed a point 1000 feet from the intersection, the driver looked at zone R for 0.2 sec. Then, the green dot indicating the driver s eye focus disappeared from the screen (i.e., zone O) for 0.5 sec beginning at a point 981 feet from the intersection. This coding method was used to characterize driver eye movement data for each of the selected segments and intersections.

8 Ko, Higgins, Chrysler, and Lord 8 TABLE 2 Example of Data Coding of Drivers Eye Movement when Approaching the Intersection Time (sec) Speed (mph) Distance (ft) Zone R C L O R O R O R O R R R R R R R R C L R R R R L Data Analysis Procedure The logistic regression analysis is a technique used to model and analyze data consisting of a dependent variable and one or more predictor variables. Logistic regression is also recommended when the independent variables do not satisfy the multivariate normality assumption (11). In the current research, the binary logistic regression model was used to predict a dependent variable from a predictor variable. This study defined a distance as the predictor variable and each zone (i.e., zone R, C, L, and O) as the dependent variables. The binary logistic regression analysis defined the probability of drivers eye movement related to each zone as a function of distance. The analysis was only performed for intersections, since the roadway segments were defined arbitrarily. In this study, the dependent variable was coded as zero for not occurring and as one for occurring. The binary logistic regression model has the following functional form: p ln(odds) = ln = α + βx Dist (1) 1 p where p = the predicted probability of the event which is coded with 1 (occur) rather than with 0 (not occur)

9 Ko, Higgins, Chrysler, and Lord 9 X Dist = the predictor variable (i.e., a distance) and α, β = estimated regression coefficients. The statistical null hypotheses (H 0 ) address the following statements: H 0 : The probability of a driver looking at each zone has not significantly changed as a function of distance to the intersections. RESULTS The results of the video data from these eleven drivers were likely affected by the particular driving task that they were performing in the original TxDOT-sponsored study. The drivers had been instructed to scan the right side of the road for the experimental signs along the route; this means that the percentage of right-side glances in this reanalysis of the video is likely to be higher than it would be during typical driving. As shown in Table 3, eleven drivers made a total of 216, 78, 24, and 30 glances to zones R, C, L, and O, respectively, when approaching Intersection 1 over a distance of 1000 feet. Drivers similarly glanced most often at zone R for all four selected highway environments. On Segments 1 and 2, drivers glanced more often and for longer durations at zone C than they did when approaching the two intersections. Glances at zone C were also more frequent and longer on Segment 2 (with street lighting) than on Segment 1 (no street lighting) and more frequent and longer approaching Intersection 2 (with street lighting) than approaching Intersection 1 (no street lighting). Drivers also glanced less often and for shorter durations at zone O (away from the road) when they were traveling the lighted Segment 2 and Intersection 2 as compared to the unlighted Segment 1 and Intersection 1. The statistical analyses on the glance pattern differences for each segment and intersection are provided in the next section.

10 Ko, Higgins, Chrysler, and Lord 10 TABLE 3 Number and Duration of Glances to Each Zone Zone Category Contents Total R C L O Total (%) 216(62%) 78(22%) 24(7%) 30(9%) 348 Number of Intersection 1 Glances Average per driver (Unlighted) Duration Total (%) 122.7(65%) 34.8(18%) 15.4(8%) 16.2(9%) (sec) Average Total (%) 204(52%) 133(34%) 34(9%) 22(6%) 393 Number of Intersection 2 Glances Average per driver (Lighted) Duration Total (%) 109.2(54%) 62.5(31%) 20.8(10%) 8(4%) (sec) Average Total (%) 130(46%) 87(31%) 29(10%) 37(13%) 283 Number of Segment 1 Glances Average per driver (Unlighted) Duration Total (%) 56.7(45%) 36(29%) 9.4(7%) 23.3(19%) (sec) Average Total (%) 133(47%) 102(36%) 23(8%) 24(9%) 282 Number of Segment 2 Glances Average per driver (Lighted) Duration (sec) Total (%) 71.6(50%) 51.5(38%) 8.6(6%) 12(8%) Average Number of Glances to Each Zone There were some differences in how many times drivers looked at each zone among the different roadway environments. Table 4 shows the results of the Wilcoxon Signed Ranks Test, a non-parametric test, on the zones among each intersection and segment. There were significant differences in the number of glances between intersections and segments at zone R and between Intersection 1 and Intersection 2 at zone C. However, for zones L and O, there was no significant difference in the number of glances between intersections and segments. As shown in Table 3 and 4, drivers made significantly more glances to the right side (zone R) of the scene when approaching the intersections than when driving the segments. The glances to the right side could be mainly affected by the presence of signs. Segment 1 and Intersection 1 have the same number of roadway signs (i.e., three signs), and Segment 2 and Intersection 2 also have the same number of roadway signs (i.e., four signs). For zone C, there was a significant difference in the number of glances between the lighted and unlighted intersections. When approaching Intersection 2 (lighted), drivers made more glances to zone C than when approaching the unlighted Intersection 1 (Table 3). Drivers also made more glances to zone C when passing Segment 2 (lighted) than when passing Segment 1 (unlighted), although the difference was not statistically significant. The most distinctive difference in glance pattern among the two intersections and two segments can be attributed to the presence/absence of street lighting. From the results of the Wilcoxon Signed Ranks Test, the proportional difference, and the roadway

11 Ko, Higgins, Chrysler, and Lord 11 characteristics, the analysis supports the result that drivers spent more time looking at the road directly in front of them (zone C) when street lighting was provided. TABLE 4 Comparison Table of p-values from Wilcoxon Signed Ranks Test on Number of Glances between Intersections and Segments (a) Zone R (b) Zone C Seg. 2 Inter. 1 Inter. 2 Seg. 2 Inter. 1 Inter. 2 Seg * 0.016* Seg Seg * 0.028* Seg Inter Inter * c) Zone L (d) Zone O Seg. 2 Inter. 1 Inter. 2 Seg. 2 Inter. 1 Inter. 2 Seg Seg Seg Seg Inter Inter Note: * p < 0.05 Binary Logistic Regression Analysis on Probabilities of Each Zone by Distance The results of binary logistic regression analysis regarding the probabilities of glances to each zone as distance to the intersection decreased are shown in Table 5. There were statistical significances for zones L and O approaching Intersection 1 and zones R, C, L, and O approaching Intersection 2. To transform the results of the regression analysis to a probability form, the processes below were performed. For example, the binary logistic regression equation for zone L in Intersection 1 is: ln( odds) = XDist (2) This equation can predict the odds of zone L for Intersection 1 based on the change of distance ( X Dist ) while driving the intersection. The odds prediction equation is: Odds Figure 2 illustrates the probabilities of each zone for intersections after converting odds to probability using equation X = e Dist (3)

12 Ko, Higgins, Chrysler, and Lord 12 TABLE 5 Results of Binary Logistic Regression Analysis between Zone Areas and Distance 95.0% C.I. for Type Zone variable B S.E. df Sig. Exp(B) EXP(B) Lower Upper Intersection 1 (unlighted) Intersection 2 (lighted) * P<0.05 R C L O R C L O Distance Constant Distance Constant Distance * Constant Distance * Constant Distance * Constant Distance * Constant Distance * Constant Distance * Constant For both intersections, the probabilities for zones L and O significantly changed as drivers got closer to the intersections (Table 5). The probabilities of glances to zone L increased as distance to the intersections decreased (Figure 2 (a) and (b)); for Intersection 1, the probability of a glance to zone L was less than 2% if the distance was 1000 ft, but it was approximately 17% if the distance was 50 ft. The probability of glances to zone L on the approach to Intersection 2 changed in a similar way with Intersection 1 as the distance to the intersection decreased. For zone O, the probability changed in the opposite direction of zone L. For both intersections, the probabilities of glances to zone O decreased with decreasing distance (Figure 2 (a) and (b)); the probability was approximately 16% if the distance was 1000 ft, but it was less than 3% if the distance was 50 ft at Intersection 1. The probability of glances to zone O on the approach to Intersection 2 decreased similarly as distance decreased. This result means that drivers made fewer glances out of screen (away from the roadway) as their distance to the intersections decreased. The traffic control signs and signals at the intersections likely cued drivers to focus on the roadway ahead (and therefore within the screen).

13 Ko, Higgins, Chrysler, and Lord 13 Intersection 1 Percent of Total Eye Glances 70% 60% 50% 40% 30% 20% 10% 0% Distance (ft) to the intersection 1 Zone R Zone C Zone L Zone O (a) Probabilities for Intersection 1 (no street lighting, stop controlled) Percent of Total Eye Glances 70% 60% 50% 40% 30% 20% 10% 0% Intersection Distance (ft) to the intersection 2 Zone R Zone C Zone L Zone O (b) Probabilities for Intersection 2 (street lighting, signal controlled) FIGURE 2 Probabilities of each glance zone by distance to the intersections. The probabilities for zones R and C behaved differently for the two intersections. At Intersection 1, the probabilities of glances to zones R and C did not change with the decreasing distance (Figure 2(a)). However, at Intersection 2, the probabilities for zone R and C significantly changed as distance to the intersection decreased. The probability of glances to zone C was about 12% if the distance was 1000 ft, but about 46 % if the distance was 50 ft; the probability of glances to zone C increased the closer the driver got to the intersection (Figure 2 (b)). Drivers made more glances to the center lane (zone C) as they approached closer to the intersection. However, the probability for zone R decreased with the distance; the probability of zone R was 63% if the distance was 1000 ft, but it was approximately 40% if the distance was 50 ft (Figure 2 (b)). Drivers made fewer glances on the right side (zone R) of the roadway as they got closer to Intersection 2.

14 Ko, Higgins, Chrysler, and Lord 14 DISCUSSION & CONCLUSIONS Eye Movement Analysis According to the research of Serafin (4), drivers glance straight ahead at the road more often than to the right or left. This study s results were different: drivers glanced most often to the right (zone R) on both the highway segments and the intersections. The reason for this difference can be explained by the objective of the original study for which the video was collected, in which the participating drivers were instructed to scan for specific road signs on the right side of the road. Although this instruction to the drivers likely altered their natural of eye movement while driving the route, the results of this new analysis of the eye-tracking videos still showed differences in driver eye movement correlating to the varying roadway characteristics. This study showed some correlation between drivers eye movements and certain roadway features. Driver eye movements were significantly different when approaching intersections than when they were driving on highway segments without intersections. Drivers glanced more often and longer at the road directly ahead (i.e., diverting some attention from the roadway signs on the right) when driving the highway segment and intersection approach with street lighting than for the segment and intersection without street lighting. Street lighting is associated with a reduction in vehicle crashes; this study concludes that one reason for this reduction in crashes is the increased probability that a driver will notice more of the road surroundings, and therefore focus his or her gaze on potential conflict or decision points (for example, the road/intersection ahead) when street lighting is present. Challenges of Re-analyzing Existing Eye-movement Data Certain characteristics of the eye-tracker data presented some challenges and limitations for the new analysis. Data were collected from only 11 of 24 drivers due to problems with the eye-tracker calibration. Although the eyetracker was calibrated to each driver before the start of the route, the calibration could degrade due to drivers head movements and vibration of the vehicle. Correcting this problem will be a key issue for research using eye-tracking devices in the future. Absent a consistent target such as the road signs that were the focus of the original study, categorizing and analyzing the locations of driver glances was more difficult. Finally, since the drivers in the original study had been instructed to scan for signs on the right side of the road, it is likely that their overall glance pattern was different than it would have been for natural driving. The experience from this study suggests that future research involving data-mining and re-analysis of existing driving data (including eye-tracking data) has the potential to yield some useful results. However, the results gleaned from this type of re-analysis should be considered in light of the purpose, procedures, and conditions of the original study. ACKNOWLEDGEMENTS This research is extracted from the project funded by Southwest Region University Transportation Center. The authors are thankful to the center for the generous financial support. Thanks also go to Sarah Young and Jesse Stanley for their assistance with data

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